Design for a Digital Twin in Clinical Patient Care
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911578931068928 |
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| author | Nitschke, Anna-Katharina Brandl, Carlos Egersdörfer, Fabian Görtz, Magdalena Hohenfellner, Markus Weidemüller, Matthias |
| author_facet | Nitschke, Anna-Katharina Brandl, Carlos Egersdörfer, Fabian Görtz, Magdalena Hohenfellner, Markus Weidemüller, Matthias |
| contents | Digital Twins hold great potential to personalize clinical patient care, provided the concept is translated to meet specific requirements emerging from established clinical workflows. We present a general and unspecialized Digital Twin design combining knowledge graphs and ensemble learning to reflect the entire patient's clinical journey and assist clinicians in their decision-making. Such a design is predictive, modular, evolving, informed, interpretable and explainable, thus opening broad clinical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01206 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Design for a Digital Twin in Clinical Patient Care Nitschke, Anna-Katharina Brandl, Carlos Egersdörfer, Fabian Görtz, Magdalena Hohenfellner, Markus Weidemüller, Matthias Software Engineering Computational Engineering, Finance, and Science Medical Physics Digital Twins hold great potential to personalize clinical patient care, provided the concept is translated to meet specific requirements emerging from established clinical workflows. We present a general and unspecialized Digital Twin design combining knowledge graphs and ensemble learning to reflect the entire patient's clinical journey and assist clinicians in their decision-making. Such a design is predictive, modular, evolving, informed, interpretable and explainable, thus opening broad clinical applications. |
| title | Design for a Digital Twin in Clinical Patient Care |
| topic | Software Engineering Computational Engineering, Finance, and Science Medical Physics |
| url | https://arxiv.org/abs/2505.01206 |